4. Summary and discussion
Internal wave signatures are apparent in variations of descent rate data
(w’ ) from Deep SOLO floats. Mean deep w’ variances (Figure
4) and dominant vertical wavelengths (Figure 5) exhibit geographical
variability tied to proximity to rough seafloor topography and deep
currents, with variations among and even within some deep basins. For
example, within the Brazil Basin, mean deep w’ variances in the
western part of the basin, where bottom roughness is very low on the
abyssal plain, are on average about 3 times lower than those in the
eastern part of the basin, where topography is very rough on the western
flank of the Mid-Atlantic Ridge. This result is consistent with earlier
findings of increased microscale energy and mixing over mid-ocean ridges
(Polzin et al., 1997). Another interesting contrast in mean deepw’ variances is the relatively high values found within the
Samoan Passage compared to the lower values immediately to the south of
that passage. This elevated activity could be partly owing to the
influence of lee waves and hydraulic jumps as bottom water flows
northward through the passage (Carter et al., 2019). The elevated mean
deep w’ variances further north of the passage could also be
partly owing to the interactions of tidal flows, which can propagate
over long distances, especially those at low vertical modes (Zhao et
al., 2016), with local rough bathymetry.
Overall, mean deep w’ variances are correlated at 0.16 with local
topographic roughness. So, as expected, internal wave activity is higher
in the vicinity of rough topography. While this correlation is not
extremely high, it is easily statistically significant (p=0.00).
Furthermore, the dominant vertical wavelengths of w’ are also correlated
at -0.07 with local topographic roughness (again, with p=0.00). This
statistically significant negative correlation is also expected as
locally generated internal waves should have shorter vertical
wavelengths than those propagating from more distant generation regions,
because the shorter vertical wavelength packets are more subject to
breaking and subsequent dissipation.
It would also be interesting to examine internal wave signatures in deep
density vertical strain using the float scientific data (temperature,
salinity, and pressure profiles), similarly to what has been done with
core Argo data (Whalen et al., 2012). However, many of these Deep SOLO
floats were set to sample at 50 dbar intervals over some of the deeper
portions of the water column to conserve float battery energy, and
sampling at more frequent intervals field (25 dbar would be sufficient)
would be required to properly estimate the deep strain. Nonetheless, the
data we have so far of deep w’ variances provides a tantalizing
glimpse of what might be learned of internal wave energy distributions
in the deep ocean from a global Deep Argo array. Already, with only a
few Deep Argo regional pilot arrays established, there are roughly three
times the number of Deep Argo profiles than were used from all of WOCE
in one study of deep mixing (Kunze et al., 2006).
Since float profiling rates typically take tens of seconds to adjust to
changes in forcing including ambient vertical velocity, float buoyancy,
and ambient density (e.g., Cusack et al., 2017), the perturbations in
their descent rates will be slightly attenuated and lagged relative to
the actual internal wave vertical velocities. However, even for the
shortest dominant vertical wavelengths (e.g. 393 dbar) and the fastest
background float descent rates (e.g. 0.17 dbar s-1),
profiling for half a vertical wavelength would take around 20 minutes,
far longer than float adjustment times. Hence, these measurement errors
should have a very small impact on the results of this study.
For internal waves generated at the sea-floor, which would have group
velocity (and energy) propagation upward, hence phase velocity (crest
and troughs) propagation downward, sampling on descent would bias
vertical wavelength estimates towards long values. For such internal
waves at the inertial period, the dominant vertical wavelengths
estimated here would be biased long by 10 (±6) %. If instead the energy
propagation were always downward, the wavelength estimates would be
biased short by 8 (±4) % at inertial periods. For internal waves with
periods shorter than inertial, these errors would be larger. However, if
energy propagation were evenly split between upward and downward
packets, these errors would instead be random, and unbiased. At any
rate, wave amplitudes should be unaffected by this issue.